A Review on Cryptocurrency Transaction Methods for Money Laundering

• URL: http://arxiv.org/abs/2311.17203v1
• Date: Tue, 28 Nov 2023 20:17:11 GMT
• Title: A Review on Cryptocurrency Transaction Methods for Money Laundering
• Authors: Hugo Almeida, Pedro Pinto, Ana Fernández Vilas,
• Abstract summary: characterization of current cryptocurrency-based methods used for money laundering are paramount to understanding the circulation flows of physical and digital money. This article may in the future help design efficient strategies to prevent illegal money laundering activities.
• Score: 2.1711205684359243
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cryptocurrencies are considered relevant assets and they are currently used as an investment or to carry out transactions. However, specific characteristics commonly associated with the cryptocurrencies such as irreversibility, immutability, decentralized architecture, absence of control authority, mobility, and pseudo-anonymity make them appealing for money laundering activities. Thus, the collection and characterization of current cryptocurrency-based methods used for money laundering are paramount to understanding the circulation flows of physical and digital money and preventing this illegal activity. In this paper, a collection of cryptocurrency transaction methods is presented and distributed through the money laundering life cycle. Each method is analyzed and classified according to the phase of money laundering it corresponds to. The result of this article may in the future help design efficient strategies to prevent illegal money laundering activities.

Related papers

• Intelligent Anti-Money Laundering Solution Based upon Novel Community Detection in Massive Transaction Networks on Spark [5.230386823973596]
  A temporal-directed Louvain algorithm has been proposed to detect communities according to relevant anti-money laundering patterns. This solution can greatly improve the efficiency of anti-money laundering work for financial regulation agencies.
  arXiv  Detail & Related papers  (2025-01-08T02:57:08Z)
• Beyond Static Datasets: A Behavior-Driven Entity-Specific Simulation to Overcome Data Scarcity and Train Effective Crypto Anti-Money Laundering Models [0.23020018305241333]
  Money laundering is a key crime to be mitigated to also suspend the movement of funds from other illicit activities. It is getting extremely difficult to identify money laundering in crypto transactions owing to many layering strategies available today. In this paper, we propose behavior embedded entity-specific money laundering-like transaction simulation.
  arXiv  Detail & Related papers  (2025-01-01T06:58:05Z)
• BlockFound: Customized blockchain foundation model for anomaly detection [47.04595143348698]
  BlockFound is a customized foundation model for anomaly blockchain transaction detection. We introduce a series of customized designs to model the unique data structure of blockchain transactions. BlockFound is the only method that successfully detects anomalous transactions on Solana with high accuracy.
  arXiv  Detail & Related papers  (2024-10-05T05:11:34Z)
• Transaction Fraud Detection via an Adaptive Graph Neural Network [64.9428588496749]
  We propose an Adaptive Sampling and Aggregation-based Graph Neural Network (ASA-GNN) that learns discriminative representations to improve the performance of transaction fraud detection. A neighbor sampling strategy is performed to filter noisy nodes and supplement information for fraudulent nodes. Experiments on three real financial datasets demonstrate that the proposed method ASA-GNN outperforms state-of-the-art ones.
  arXiv  Detail & Related papers  (2023-07-11T07:48:39Z)
• Blockchain Large Language Models [65.7726590159576]
  This paper presents a dynamic, real-time approach to detecting anomalous blockchain transactions. The proposed tool, BlockGPT, generates tracing representations of blockchain activity and trains from scratch a large language model to act as a real-time Intrusion Detection System.
  arXiv  Detail & Related papers  (2023-04-25T11:56:18Z)
• Catch Me If You Can: Semi-supervised Graph Learning for Spotting Money Laundering [0.4159343412286401]
  Money laundering is a process where criminals use financial services to move illegal money to untraceable destinations. It is very crucial to identify such activities accurately and reliably in order to enforce an anti-money laundering (AML) In this paper, we employ semi-supervised graph learning techniques on graphs of financial transactions in order to identify nodes involved in potential money laundering.
  arXiv  Detail & Related papers  (2023-02-23T09:34:19Z)
• Fighting Money Laundering with Statistics and Machine Learning [95.42181254494287]
  There is little scientific literature on statistical and machine learning methods for anti-money laundering. We propose a unifying terminology with two central elements: (i) client risk profiling and (ii) suspicious behavior flagging.
  arXiv  Detail & Related papers  (2022-01-11T21:31:18Z)
• CubeFlow: Money Laundering Detection with Coupled Tensors [39.26866956921283]
  Money laundering (ML) is the behavior to conceal the source of money achieved by illegitimate activities. Most existing methods detect dense blocks in a graph or a tensor, which do not consider the fact that money are frequently transferred through middle accounts. CubeFlow proposed in this paper is a scalable, flow-based approach to spot fraud from a mass of transactions.
  arXiv  Detail & Related papers  (2021-03-23T09:24:31Z)
• Machine learning methods to detect money laundering in the Bitcoin blockchain in the presence of label scarcity [1.7499351967216341]
  We show that existing state-of-the-art solutions using unsupervised anomaly detection methods are inadequate to detect the illicit patterns in a real Bitcoin transaction dataset. Our proposed active learning solution is capable of matching the performance of a fully supervised baseline by using just 5% of the labels.
  arXiv  Detail & Related papers  (2020-05-29T15:52:48Z)
• Pump and Dumps in the Bitcoin Era: Real Time Detection of Cryptocurrency Market Manipulations [50.521292491613224]
  We perform an in-depth analysis of pump and dump schemes organized by communities over the Internet. We observe how these communities are organized and how they carry out the fraud. We introduce an approach to detect the fraud in real time that outperforms the current state of the art.
  arXiv  Detail & Related papers  (2020-05-04T21:36:18Z)
• Characterizing and Detecting Money Laundering Activities on the Bitcoin Network [8.212945859699406]
  We explore the landscape of potential money laundering activities occurring across the Bitcoin network. Using data collected over three years, we create transaction graphs and provide an analysis on various graph characteristics to differentiate money laundering transactions from regular transactions. We propose and evaluate a set of classifiers based on four types of graph features to classify money laundering and regular transactions.
  arXiv  Detail & Related papers  (2019-12-27T11:34:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.